Die GBG Mannheim (bzw. GBG – Mannheimer Wohnungsbaugesellschaft mbH) mit Sitz in Mannheim ist die größte Wohnungsbaugesellschaft in Baden-Württemberg. Mit ihrem Bestand von rund19.400 Wohnungen bewirtschaftet die GBG Mannheim 13 % aller Wohnungen in der Stadt Mannheim. Natürlich ist die GBG ein fester Bestandteil des Mannheimer Lebens. 

Oben: Ein GBG-Projekt in Mannheim. Quelle: GBG

Neben der Bereitstellung von Wohnraum hat die GBG noch weitere Ziele für die Kommune. Mit einer über 95-jährigen Geschichte engagiert sich die GBG für grüne, urbane und nachhaltige Initiativen, sei es in Form von gemeinschaftlichen Außenanlagen, Sonnenkollektoren auf ihren Gebäuden oder bezahlbarem Wohnraum.  

Bei so vielen Liegenschaften und MieterInnen hat die GBG einen enormen Datenstrom zu bewältigen, um den Überblick über Verträge, ihren Wohnbestand, Service-Tickets, Projektinformationen und Marketingdaten zu behalten. Um eine Organisation wie die GBG effizient und effektiv zu führen, müssen diese Daten in Erkenntnisse und Entscheidungen auf allen Ebenen umgewandelt werden, von der langfristigen strategischen Vision bis hin zur operativen Umsetzung.  

Traditionell nutzte die GBG SAPs eigene Berichtsfunktionalitäten sowie Excel und SharePoint, um ihre Datenfragen zu beantworten. Um auch in Zukunft wachsende Berichtsanforderungen abbilden und Limitationen von bestehenden Systemen überwinden zu können, wurde eine entsprechende Lösung gesucht. Beispielsweise waren Excel-Auswertungen meist stichtagsbasiert, was die Aufbereitung historischer Daten zur Erkennung von Trends und Mustern erschwerte.  

Die GBG beschloss, eine neue Ära der Analytics und Business Intelligence einzuläuten, indem das Unternehmen einen Analytics-Stack implementierte, der vernetzt, benutzerfreundlich, skalierbar und vor allem effizient genug war, um die Berichterstellung von Wochen auf Tage oder sogar Stunden zu reduzieren. Für dieses ehrgeizige Ziel wandte sich die GBG an InterWorks. 

Neue Infrastruktur für neue Einblicke 

Als die GBG Tochtergesellschaft ServiceHaus, InterWorks in ihr Projekt einbezog, wurde das Ziel, den Analytics-Stack auf den neuesten Stand der Technik zu bringen, festgelegt – ein Ziel, bei dem InterWorks mehr als bereit war, zu helfen. 

Zu Beginn entschied sich InterWorks für einen risikoarmen, schnellen Proof of Concept, bei dem wir Snowflake und Matillion auf der Azure-Plattform einrichteten, Quelldaten von SAP in Snowflake luden und dann alle diese Daten mit Tableau verbanden. Auf diese Weise konnte die ServiceHaus bereits zu Beginn des Projekts die Machbarkeit der Lösung bewerten und die Grundlage für künftige Entwicklungen schaffen. 

Darüber hinaus unterstützte InterWorks die ServiceHaus bei der Erstellung skalierbarer Datenladestrukturen, die Konfigurationstabellen nutzen, so dass keine fortgeschrittenen Data-Engineering-Kenntnisse erforderlich sind, um weitere Datenquellen hinzuzufügen. InterWorks hat sich außerdem verpflichtet, den Datenschutz in den Mittelpunkt dieser Entwicklung zu stellen und die DSGVO und andere gesetzliche Anforderungen einzuhalten.  

Zufrieden mit dem Proof-of-Concept gab die ServiceHaus InterWorks grünes Licht für die vollständige Implementierung des Data Stack in ihre Umgebung und öffnete damit die Tür zu neuen Dimensionen der Datenanalyse. 

Oben: Referenzarchitektur für die Datenpipeline der GBG Mannheim.

Eine Zukunft mit besseren und schnelleren Ergebnissen 

Mit dieser aktualisierten Analytics-Pipeline kann die GBG die Grenzen ihrer bisherigen Software überwinden: Daten, die früher dezentral in Excel exportiert wurden, sind jetzt in einer zentralen, validierten und schnellen Datenbank verfügbar: Snowflake. Dies erhöht die Zeitersparnis und setzt Ressourcen frei, um Fragen mit Daten zu beantworten, anstatt Daten in Excel-Tabellen aufzubereiten.  

Auch die Nachverfolgung von historischen Entwicklungen in den Daten ist nun möglich, was zu tieferen Einblicken in Datentrends im Laufe der Zeit führen kann, die vorher nicht verfügbar waren. Durch die Integration von Tableau wurden die Analysefunktionen der GBG in ähnlicher Weise vertieft, da sie nun Zugriff auf Daten haben, die im SAP-ERP-System praktisch nicht zugänglich waren. Dank der intuitiven Oberfläche von Tableau können AnwenderInnen auch ohne Datenbankkenntnisse erweiterte Analytics-Dashboards erstellen, die die Entscheidungsfindung unterstützen.  

Ein moderner Data Stack führt zu schnelleren Erkenntnissen und einem agileren Ansatz für die Datenanalyse. So einfach ist das. Jetzt kann sich die GBG weniger auf die Dateninterpretation und mehr auf die Unterstützung ihrer MieterInnen, ihrer Gemeinde und ihrer Ziele mit all diesen Daten konzentrieren.

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Optio ist ein zukunftsorientier, spezialisierter MGA (Managing General Agent) mit Sitz in London und einer globalen Reichweite. Optio entwickelt Produkte, Dienstleistungen und Lösungen, die sich positiv auf den Versicherungssektor auswirken. 

Die größte Stärke von Optio ist die Fähigkeit, Marktspezialisten mit intelligenter Technologie auszustatten, die es ihnen ermöglicht, in den Bereichen Versicherungsprodukte, Versicherungsvertretung und Maklerdienste innovativ zu sein – und gleichzeitig starke Beziehungen zu Geschäftspartnern aufzubauen. Bei diesen Partnern handelt es sich in erster Linie um professionelle Versicherungsmakler, deren Zeichnungskapital hauptsächlich von Lloyd’s of London bereitgestellt wird. 

Optios einzigartige Kombination aus Vision und Ausführung hat dem Unternehmen den Ruf eines Marktführers in zahlreichen Sektoren eingebracht, insbesondere in den Bereichen Nuklearindustrie, Zahlungsverzug von Subunternehmern, Krisenmanagement und Fusionen und Übernahmen. 

Eine moderne Vision der Datenarchitektur

Das Lebenselixier des Versicherungssektors sind Daten, und Optio betrachtet Daten seit langem als ihre wertvollste Währung. Sie fließen in jede Ebene der Entscheidungsfindung ein und stehen in direktem Zusammenhang mit der Fähigkeit, Kapital von Geschäftspartnern zu erhalten. 

Natürlich ist dies kein leichtes Unterfangen. Wie viele ihrer Kollegen im Versicherungssektor erfahren haben, ist die Wissenschaft und Kunst der Vereinheitlichung von Versicherungsdaten oft zu kompliziert und veraltet. Es war nicht mehr tragbar, eine ständig wachsende Liste von Datenquellen in Excel und Access zusammenzuführen. Hinzu kommt, dass die Geschäftspartner von Optio unterschiedliche Plattformen zur Datenerfassung und -auswertung nutzen, was die Herausforderung der Integration noch größer macht. 

Nach Prüfung der möglichen Optionen entschied Optio im Jahr 2020, dass der Aufbau eines Data Warehouses der beste Weg in die Zukunft ist. Dieses Data Warehouse sollte plattformunabhängig sein, um die große Vielfalt der Quellen, die in das Data Warehouse einfließen, zu berücksichtigen. Neben der nahtlosen Integration der Daten strebte Optio auch die Automatisierung von Berichten an, damit die Endbenutzer nicht lange suchen müssen, um die für sie relevanten Erkenntnisse zu gewinnen. Schließlich sollte das Data Warehouse skalierbar sein und das Unternehmen bei neuen Plattformen, Neueinstellungen und Übernahmen unterstützen können. 

Mit der umfassenden Vision ihres Data Warehouses fertig, bestand der nächste Schritt für Optio darin, ein erfahrenes Business Intelligence-Team aufzubauen, das die Datenarchitektur entwickelt, bereitstellt und pflegt. Dieses Team sollte auch mit Experten von Drittanbietern zusammenarbeiten, um sicherzustellen, dass die Architektur so robust wie möglich ist. Die Leitung dieses Teams übernahmen der CIO von Optio, Kevin Cleary, und der BI-Berater Meishan Nunes. Wichtige Unterstützung erhielten sie von IT-Leiter Carlos Nunes, Dateningenieur Andy Dennison und Berater Sean Olliffe. Sean Olliffe arbeitet mit Optio an der Entwicklung einer Reihe von SharePoint-Sites, um alle Anforderungen von Optio an die Dokumentenverwaltung in einer robusten und dennoch skalierbaren Umgebung unterzubringen. 

Das Team entschied sich schon früh für Snowflake und Matillion als Rückgrat seiner neuen Architektur, erkannte aber schnell, dass es Unterstützung von Experten benötigte, um diese Lösungen einzurichten und sicherzustellen, dass sie sich gut in das kürzlich erworbene Tableau Online für geschäftsbezogene Analysen integrieren lassen. Glücklicherweise kannten sie genau den richtigen Partner für diese Aufgabe – InterWorks. Nachdem InterWorks bereits bei der Evaluierung von Tableau Online geholfen und das Unternehmen regelmäßig mit Assist by InterWorks unterstützt hatte, dauerte es nicht lange, bis sich die Gespräche auf das neue Datenarchitektur-Projekt verlagerten. Angesichts der umfassenden Erfahrung von InterWorks mit Snowflake und Matillion war die Entscheidung, uns in das größere Datenarchitektur-Projekt einzubinden, absolut sinnvoll. Auf Seiten von InterWorks waren Strategic Account Executive Richard Smith, Data Engineer Chris Hastie und Analytics Consultant Robin Bergmans für das Projekt verantwortlich. 

“InterWorks wurde aufgrund seiner Erfahrung und seiner Verwurzelung im Bereich der Daten- und BI-Lösungen ausgewählt und wurde schnell zu einem vertrauenswürdigen Partner von Optio. Dies galt sowohl aus technischer als auch aus ästhetischer/präsentativer Sicht, denn unser Ziel war es, nicht nur ein Data Warehouse und nicht nur eine funktionale Reporting-Suite zu liefern, sondern auch einen ‘Wow’-Moment für interne und externe Nutzer.”

Kevin Cleary, CIO, Optio

Implementierung eines kompletten Stapels von Datenlösungen

Als Grundlage der neuen Datenarchitektur von Optio dient Snowflake, das über die Funktion eines reinen Data Warehouse hinausgeht und als vollständige Datenplattform in der Cloud dient, auf der Optio Daten aus verschiedenen Datenquellen benutzerfreundlich und skalierbar Daten aufnehmen und zusammenführen kann. Dazu gehören wichtige Finanzdaten von Total Objects, Buchhaltungsdaten von Xero und proprietäre Systeme wie Optio Connect, dass eine Reihe von Kernproduktlinien des Konzerns enthält. Wenn Optio sein Geschäft weiter ausbaut, kann das Unternehmen problemlos neue Datenquellen hinzufügen und seine Snowflake-Kapazität bei Bedarf entsprechend erweitern. 

Im Rahmen von Snowflake arbeitete InterWorks mit dem Optio SharePoint-Team zusammen und lieferte Anleitung und technischen Input für eine Reihe von innovativen Lösungen. Durch diesen Input wurden neue Lösungen geschaffen und wertvolle Workflow-Effizienzen erzielt, wobei neue Software und Techniken zum Einsatz kamen, die sich positiv auf ein weiteres geschäftskritisches Projekt von Optio ausgewirkt haben. 

Sobald die Daten in Snowflake vorliegen, müssen sie im nächsten Schritt für die Analyse vorbereitet werden, um einen nahtlosen Übergang zu Tableau zu gewährleisten. An dieser Stelle kam Matillion als ideales Data Prep-Tool ins Spiel. Cloud-basiert und benutzerfreundlich, sparte Matillion unzählige Stunden manueller Entwicklung und stellte sicher, dass der Datenfluss effizient und ohne Unterbrechung verlief. 

Schließlich kam der visuellste und benutzerorientierteste Aspekt der neuen Datenarchitektur ins Spiel: Tableau. Da Optio bereits mit Tableau gearbeitet hatte, war das Unternehmen mit den leistungsstarken Analysefunktionen vertraut, insbesondere mit den scharfen Datenvisualisierungen. Mit Curator von InterWorks konnte Optio die bereits in Tableau erstellten Dashboards in eine vollständig anpassbare und gebrandete Weboberfläche einbinden, was diese Erfahrung noch weiter verbesserte. Die Möglichkeit, jeden Aspekt des Benutzererlebnisses individuell zu gestalten, ist ein entscheidender Schritt, um sicherzustellen, dass die Endbenutzer schnell und organisch relevante Erkenntnisse gewinnen können. 

“Als die Person, die für die Genehmigung der Ausgaben und die Durchführung dieses Projekts verantwortlich war, fand ich die Zusammenarbeit mit den Mitarbeitern von InterWorks (insbesondere Chris Hastie und Richard Smith) großartig. Ich arbeite in unserem gesamten Unternehmen mit vielen Anbietern zusammen, und ich habe mich immer auf unsere Gespräche gefreut, da Chris und Richard die Vision sofort verstanden haben und nie von dieser Vision abgewichen sind. Sie haben – zusammen mit Meishan, Carlos und Andy – einfach alles möglich gemacht.” 

Kevin Cleary, CIO, Optio 

Tieferes Eintauchen in die Daten

Mit der neuen Datenarchitektur hat Optio das Data Warehouse als zentrale Komponente in seinem digitalen Ökosystem etabliert. Außerdem hat das Unternehmen bereits einen strategischen Fahrplan für die nächsten Schritte erstellt. Ein großes Ziel ist es, so viel wie möglich zu automatisieren. Zu diesem Zweck haben sie bereits ein neues Projekt mit dem Lloyd’s Lab-Absolventen DistriBind begonnen, um die Produktion von Risiko- und Prämien-Borderos direkt aus ihrer Datenarchitektur zu automatisieren. Die Steigerung der Effizienz, der Genauigkeit und der Zeitersparnis ist nicht nur in der Gegenwart sinnvoll, sondern wird auch zur Zukunftssicherheit des Unternehmens beitragen. Diese und viele andere vorgeschlagene Dateninitiativen konzentrieren sich alle auf die Datenarchitektur, die darauf ausgerichtet ist, Menschen zu unterstützen, und unterstreichen Optios Leitbild, Menschen und Technologie zu verbinden. 

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Founded in 1978, Downtown Women’s Center (DWC) exists to end homelessness for women in the Greater Los Angeles area. They achieve this through a variety of impactful initiatives and programs, including supporting women through permanent housing and community-based rapid re-housing, a robust health clinic that exclusively services women in the Skid Row community, a multi-faceted workforce development program and much more. In addition to all the direct services and programs they provide, DWC is also a national advocacy leader in raising awareness regarding women experiencing homelessness.

You can listen to the full, audio version our this case study below or keep scrolling for the text version.

Using Data to Increase Resources for Women

Data has played an integral role at DWC over the years. It informs virtually every decision they make, and they even have a dedicated focus on doing community-based research. A notable example of this research is the Women’s Needs Assessment that they developed in 2001. The goal behind this assessment was to home in on what women within the Skid Row community were experiencing, how they access resources, incidents of violence and more. The assessment occurs every three years, and in 2019, DWC expanded its scope to be city-wide – no small feat considering the population of LA alone is around four million people.

“This has been immensely helpful for Los Angeles to help develop strategies and programs that directly impact women. But we’re also working on getting unaccompanied women recognized statewide – unaccompanied women would be women that are seeking services without children or another dependent. As a group, they don’t have a lot of dedicated services. We know that roughly 1/3 of the homeless population in Los Angeles are women, but there is nowhere near 1/3 of the resources set aside for women.”

– Lorena Sanchez, Chief Development and Communication Officer, Downtown Women’s Center

Because the Women’s Needs Assessment has been so successful in LA, it’s since been replicated in cities like Washington, D.C. and Chicago. DWC also aims to expand their survey to all of Los Angeles County, more than doubling their census from LA alone. Point being, DWC is forward-thinking with research and advocacy, as well as the unique role data plays within those realms. In that same spirit, a chance encounter with a volunteer in 2020 led to even more impactful data work within their organization.

When Volunteers Meet Data

DWC employs an incredibly talented and dedicated staff to run their various programs, but volunteers also play a considerable role in DWC’s operations and overall mission. These volunteers serve in several different capacities, from volunteering at special events to DWC’s onsite café. Such as was the case with DWC volunteer and InterWorks Analytics Consultant Sarah Dorfman.

As part of the Downtown LA community, Sarah was passionate about lending her talents to such a worthy organization. The more Sarah got involved, the more DWC learned about her unique skillset. It wasn’t long until Sarah was looped in to help DWC’s Workforce Development Director Kindalle Brown on sprucing up their data trackers – a key tool in helping Workforce Development communicate the efficacy of their programs.

“My experience with data at DWC, specifically in the Workforce Development Department, was that we collected a lot of data, we collected a lot of numbers and performance metrics, and we didn’t necessarily know what that data was telling us.”

– Kindalle Brown, Workforce Development Director, Downtown Women’s Center

Sarah started out by participating in broad discussions with Kindalle and DWC Chief Development and Communication Officer Lorena Sanchez about the scope of their efforts as well as the data goals of DWC, looking to see what resources she might be able to bring. She was originally going to improve the trackers on her own time, but after discussion with InterWorks CEO Behfar Jahanshahi, the work was approved as a full-fledged pro bono project. What’s more, Sarah was able to bring in additional help from InterWorks Analytics Consultant Ben Calder and former InterWorks BI Intern John Moss, forming a dedicated team to overhaul their data trackers.

Upon kicking off the project, the InterWorks team found five to six different combinations of Google Sheets with different tabs in each one. The primary goal of the team was to consolidate these multiple workbooks into a single workbook with multiple tabs that would be uniformly understood within the Workforce Development department. The team made sure that each component of the workbook has a data dictionary where term definitions could reside. Additionally, the team built the workbook with tabs that could be manually input by Kindalle and her team as well as tabs that automatically summarize data and provide trend analysis. In addition to building out this new framework for the tracker, Sarah also dove deep into DWC’s data sources to deduplicate it where applicable and provided additional data quality checks in conjunction with DWC staff.

The New Workforce Development Tracker in Action

After consolidating their data and releasing a trial version of the new Workforce Development Tracker, it didn’t take long for Kindalle and her team to notice immediate results. For example, the Workforce Development department is required to submit quarterly reports on all their initiatives. Prior to the new tracker being released, submitting these reports was a bit of a hassle that required a lot of manual entry. What’s more, all that manual effort limited their ability to be proactive with their data efforts. The advent of the new tracker, even in its trial form, allowed Kindalle and other coordinators the ability to get their number in on time without the hassle.

“The clarity that the tracker InterWorks created for us gave us the information we needed, but also gave us the ability to see where we needed to focus and where things were dropping off and where we wanted to move, which was huge. The ease with which we’re able to look at our data and be able to tell a story has improved exponentially.”

– Kindalle Brown, Workforce Development Director, Downtown Women’s Center

Streamlining and optimization weren’t the only benefits of the new data tracker. One of the biggest advantages of spending less time on reporting is that the Workforce Development department can now spend more time doing the work they love. Additionally, being able to find clear answers quickly enables them to be more transparent within their organization and with supporters of DWC.

“When we’re able to turn around and communicate our impact, that just helps with funding and helps bring attention to women’s homelessness. I think it creates that kind of, hopefully, virtuous cycle where we’re continuing to demonstrate how our programs work, why they need to be supported and how we’re changing people’s lives by helping them at all these different points.”

– Lorena Sanchez, Chief Development and Communication Officer, Downtown Women’s Center

Expanding Outreach and Equity with Data

Now that data is up to date and easily accessible for the Workforce Development department, Kindalle and her team are now focusing on expanding their outreach programs as well as the number of women that they serve. Their LA Rise program is an excellent example of a program with significant reach that they’re now able to devote more time towards. Expanded reach isn’t the only priority they’re spending more time on. With a clearer window into demographic data, they’re placing more emphasis on becoming more equitable across all their services. Even before Kindalle and Lorena arrived at DWC, there existed the Equity Dashboard which analyzed metrics and strategies across every department at DWC with the goal of deepening those to make them more equitable.

“I think probably the most common thing that companies are doing is bringing in an expert to talk about implicit bias, which is great – we all need to do that at regular intervals – but I think when you apply data, it’s like you’re tying it to resources. For us, it’s still early days. We’re trying to figure out if we’re measuring the right things, but I think it’s a fantastic example of how data can drive systemic and social change.”

– Lorena Sanchez, Chief Development and Communication Officer, Downtown Women’s Center

Progress Involves Everyone

By simply taking a closer look at their data and leaning on the unique skills of staff like Kindalle and Lorena, as well as volunteers like Sarah and countless others, DWC has put themselves in a position of strength when it comes to understanding insights on the people they serve and acting on that information. From a broader perspective, DWC is simply a fantastic organization committed to uplifting an entire community of women – an organization that InterWorks is proud to be associated with. Of course, there’s always more work to be done, and DWC is always looking for those willing to help. If you’re in the LA area and want to get involved, there are many ways you can do so:

“We’re always seeking volunteers. The easiest examples are coming down and helping in the kitchen or donating gently used items […] Thinking about what you have to offer and approaching us with ideas is also great. And we understand that not everyone is able to give financially, but if you can, even as little as $5 or $10 a month goes a long way to maintain our financial sustainability. So, all of that is appreciated.”

– Lorena Sanchez, Chief Development and Communication Officer, Downtown Women’s Center

To get involved or learn more about DWC’s mission, visit their official website.

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Based in Mannheim, Germany, GBG Mannheim (or rather GBG – Mannheimer Wohnungsbaugesellschaft mbH) is the largest housing association in the German federal state of Baden-Württemberg. With its impressive resume of 19,000 flats, GBG Mannheim accounts for 13% of all flats in the municipality of Mannheim. Needless to say, GBG is a staple of Mannheim life.

Above: A GBG project in Mannheim. Source: GBG.

In addition to providing housing, GBG has other goals for the community. With a history stretching back over 90 years, GBG has been committed to green, urban and sustainable initiatives, be it communal outdoor areas, solar panels on their buildings or affordable housing.

With so many properties and tenants, GBG has a massive influx of data to keep track of contracts, real estate portfolio, service tickets, project information and marketing data. To run an association like theirs efficiently and effectively requires turning that data into insights and decisions on all levels, from strategic long-term vision to operational execution.

Traditionally, GBG used SAP’s own reporting functionalities, as well as Excel and SharePoint, to answer their data questions. To meet growing reporting requirements in the future and to overcome limitations of existing systems, an appropriate solution was sought. For example, Excel evaluations were mostly based on snapshots of data, which made it difficult to analyze historical data in order to identify trends and patterns.

GBG decided to trek into a new era of analytics and business intelligence by implementing an analytics stack that was interconnected, easy to use, scalable and, most importantly, efficient enough to reduce report generation from weeks to days or even hours. For such an ambitious goal, GBG turned to InterWorks.

New Infrastructure for New Insights

When the GBG subsidiary ServiceHaus brought InterWorks into their project, they solidified their goal of updating their analytics stack to the modern era—a goal that InterWorks was more than happy to assist with.

To start, InterWorks decided on a low risk, quick proof of concept where we set up Snowflake and Matillion on the Azure platform, loaded source data from SAP into Snowflake, then connected all that data into Tableau. This allowed ServiceHaus to assess the feasibility of the solution early into the project and laid the foundation for any future developments.

In addition, InterWorks helped ServiceHaus build scalable data loading patterns that leveraged configuration tables, so no advanced data engineering knowledge was required to add additional sources. InterWorks also committed to data privacy being at the core of this build and stayed in line with GDPR and other legal requirements.

Satisfied with the proof of concept, ServiceHaus gave InterWorks the go ahead to fully implement the data stack into their setup and open the door to new heights of data analysis.

Above: Reference architecture for the GBG Mannheim data pipeline.

A Bold New Future with Better, Faster Results

With this updated analytics pipeline, GBG was no longer bound by the limits of their old software. Data that used to be exported to Excel is now available in a central, validated and fast database: Snowflake. This increases on saved time and frees up resources to answer questions with data, instead of wrangling data in Excel spreadsheets.

History tracking in their data insights is also now possible, which can lead to deeper insights into data trends over time that weren’t available before. Adding Tableau to the stack similarly deepened GBG’s analytics capabilities by giving them access to data that was effectively locked up in the SAP ERP system. Tableau’s intuitive interface enables users without any database knowledge to create advanced analytics dashboards that drive decision making.

A modern data stack leads to faster insights and a more agile approach to data analytics. It’s as simple as that. Now, GBG can focus less on data interpretation and more on supporting their tenants, their community and their goal with all that data.

The post GBG Mannheim appeared first on InterWorks.

Based in Grand Junction, Colorado, Quality Health Network’s (QHN) mission is to connect people for better health. Using their award-winning health information exchange (HIE), they help hospitals and healthcare providers in western Colorado and southeast Utah securely share patient health information to improve coordination throughout the continuum of patient care. By streamlining this communication, reducing duplication of services and focusing on quality care, QHN can help those providers effect better patient outcomes. In addition to their HIE, QHN also operates their Community Resource Network (CRN)—a social information exchange (SIE) designed to fill gaps and improve the health of communities overall.

Making the Switch to Tableau Software

Seeing as how QHN is in the business of electronic health data, analytics has always been a key component of their business. With over 4,900 users within their HIE serving upwards of one million patients, the amount of data QHN interacts with on a daily basis is massive. Add to that the unique standards and guidelines intrinsic to the healthcare industry, and you have a complex analytics environment that needs to operate within very specific parameters.

Of course, QHN has always been keen to keep their analytics stack as modern as possible. Even with their existing cloud-based BI platform, however, they found that there were limitations to what they could do. For example, they had long relied on sending PDF reports to their users but knew they eventually wanted to send reports directly to those people instead. To do this on their existing platform would be an expensive addition.

So, QHN set out to look for an analytics platform that could better meet their needs. This effort was led by their Director of Analytics, Jason McRoy, who had considerable experience with different analytics platforms. Under his guidance, the conversation around Tableau Software began, and Tableau was chosen as the new analytics anchor for their stack. From there, QHN would figure out what Tableau could integrate with to provide more robust analytics. It was in this phase that Tableau recommended InterWorks as an adept services partner to help integrate Tableau into QHN’s broader analytics stack.

Compounding Success with Matillion and Snowflake

Initially, QHN had intended to use an all-in-one data warehouse and BI platform to set up a traditional environment where they would host their own SQL Server and then plug Tableau into that. After conversations with InterWorks Data Lead Michael Treadwell and Account Executive Karlee Cline, a different path forward emerged.

Michael brought two new players into the conversations that would make life for QHN much easier: Snowflake and Matillion. Snowflake would give QHN a cloud data platform that was lighter to implement and more scalable than a traditional data warehouse. Plus, with Snowflake, there would be no hardware QHN would have to support. When the conversation shifted to ETL, Matillion was a natural fit. Also based in the cloud, Matillion’s real strength—beyond its user-friendly GUI—is its ability to easily ingest and build workflows around variant data. This was especially attractive to QHN because most of their data flows include variant data like HL7, which has historically been a challenge to work with using traditional tools and processes.

Eager to learn more and see Snowflake and Matillion in action, QHN requested a proof of concept (POC) from InterWorks, and Michael Treadwell was happy to oblige. In delivering this POC, there were specific things that really sold both Snowflake and Matillion as the right fit for QHN’s needs.

The big takeaway of Snowflake was that it blended multiple products and a lot of strong functionality into one platform. You have Oracle-based functions, Postgres and tSQL functions in the same place, as well as the ability to work with JSON and XML datasets. Because QHN extracts a lot of data from semi-structured content, Snowflake’s robust functionality was a huge plus. At a higher level, the POC showed just how easy it was to scale Snowflake while providing full cost transparency.

Where Matillion really shone was with its wide variety of data connectors, working with MongoDB, Postgres DB and flat files. Because QHN works with all these variations to some degree, those data connectors were a huge selling point. Of course, Matillion’s ability to streamline and automate workflows also made a solid case for the tool’s utility.

“The staff we worked with at InterWorks – Tim Rhymer, Michael Treadwell, Justin Lemmon – are all really talented, know the tools and are down to earth. They understood our circumstances as a company and went above and beyond to get the work done and get it done right, which gave us a lot of confidence that these efforts would end up where we wanted them to.”

– Jason McRoy, Director of Analytics, QHN

Making Progress with a Modern Analytics Stack

More often than not, progress with data architecture projects takes considerable time, but the Snowflake and Matillion POC took only three months to establish a strong foundation and produce results. At the end of the project, QHN had imported most of their data sources, built an initial data warehousing layer and changed their data-capture and history-capture methods for every connected source.

Building this foundation has enabled QHN to pivot their focus to uncovering new streams of data from providers, subjects and diagnoses with the goal of quickly generating reports within Tableau. The ability to rapidly prototype and test out new reports to see if they are accurate and resonate with stakeholders is an incredible leap for QHN; as a small team, they don’t have the bandwidth to sink time into massive projects. On top of all that, their new technology stack provides these advantages while being budget-conscious, flexible and scalable.

For QHN’s customers, the streamlined data architecture and agile reporting translates to more meaningful insights that can help them run more efficiently and influence better patient experiences. By exposing more streams of data and deeper insights, QHN is able to provide customers with a more useful product on several different levels.

“When we first estimated what we’d need out of a legacy environment, it was clear that it couldn’t handle the full scope of data we wanted to feed into it. With Snowflake and Matillion, however, we were able to load it all and now have more data than ever before. This helps the picture we paint for our customers to be that much richer.”

– Jason McRoy, Director of Analytics, QHN

The post Quality Health Network appeared first on InterWorks.

Optio is a forward-thinking, specialty MGA (Managing General Agent) based in London with a truly global reach. Optio develops products, services and solutions that positively impact the insurance sector.

Optio’s greatest strength is their ability to empower market specialists with smart technology, allowing them to innovate across specialty insurance products, underwriting services and broking – all while building strong relationships with business partners. Those partners are primarily professional insurance brokers, and their underwriting capital is mainly supplied by Lloyd’s of London.

Optio’s unique combination of vision and execution has earned them a reputation as a market leader within numerous sectors, most notably Nuclear, Sub-Contractor Default, Crisis Management, and Mergers and Acquisitions.

A Modern Vision of Data Architecture

The lifeblood of the insurance sector is data, and Optio has long viewed data as their most valuable currency. It informs every level of their decision making and is directly linked to their ability to secure capital from their business partners.

Of course, this is no easy task. As experienced by many of their peers in the insurance sector, the science and art of unifying insurance data is often overcomplicated and antiquated. Bringing together an ever-growing list of data sources into Excel and Access simply wasn’t sustainable anymore. What’s more, Optio’s business partners use different platforms of their own to acquire and report on data, making the challenge of integration even more sizable.

After exploring the potential options, Optio decided in 2020 that building out a data warehouse would be the best path forward. Firstly, this data warehouse would need to be platform-agnostic in order to accommodate the wide variety of sources flowing into it. In addition to seamlessly integrating their data, Optio also sought report automation so that end users wouldn’t have to dig deep to find the relevant insights they need. Finally, the data warehouse would need to be scalable and capable of supporting them as they add new platforms, hires and acquisitions.

With the broad vision of their data warehouse set, the next step for Optio was to build an experienced business intelligence team to develop, deliver and maintain their data architecture. This team would also work with third-party experts to ensure the architecture is as robust as possible. Leading this team were Optio CIO, Kevin Cleary, and BI Consultant Meishan Nunes. They received critical support from Head of IT, Carlos Nunes, Data Engineer Andy Dennison and Consultant Sean Olliffe. Sean is working with Optio to develop a series of SharePoint sites to house all of Optio’s document management requirements within a robust yet scalable environment.

The team identified Snowflake and Matillion as the backbone of their new architecture early on, but they quickly realised that they might need some expert assistance standing these solutions up and ensuring they integrated well with the recent purchase of Tableau Online for business-facing analytics. Fortunately, they knew just the partner to help – InterWorks. Having helped with their evaluation of Tableau Online and regularly assisting them via Assist by InterWorks, it wasn’t long until talks shifted towards the new data architecture project. Given InterWorks’ extensive experience with both Snowflake and Matillion, the decision to bring us into the larger data architecture project made perfect sense. Running point on the InterWorks side of things were Strategic Account Executive Richard Smith, Data Engineer Chris Hastie and Analytics Consultant Robin Bergmans.

“InterWorks were selected due to the experience and pedigree in the data/BI solution arena and quickly became a trusted partner of Optio’s. This was from a technical perspective as well as from an aesthetic/presentational perspective, as our goal was to provide not just a data warehouse and not just a functional reporting suite, but a ‘wow’ moment for internal and external users.”

– Kevin Cleary, CIO, Optio

Implementing a Full Stack of Data Solutions

Serving as the foundation of Optio’s new data architecture is Snowflake, which goes beyond the function of a mere data warehouse and serves as an entire cloud data platform where Optio can ingest and collate data from various data sources in a way that’s user friendly and scalable. This included key financial data from Total Objects, accounting data from Xero and proprietary systems like Optio Connect, which houses a number of core group product lines. As Optio continues to grow their business, they will be able to add new data sources with ease and increase their Snowflake footprint in tandem if need be.

Working within Snowflake, InterWorks worked with the Optio SharePoint team and has provided guidance and technical input on a series of innovative solutions. This input created new solutions and gained valuable workflow efficiencies, utilising new software and techniques that have positively impacted another mission-critical project for Optio.

Once the data makes it to Snowflake, the next step is to prepare it for analysis and ensure a seamless flow to Tableau. That’s where Matillion came in as the ideal data transformation tool. Cloud-based and user-friendly, Matillion saved countless hours of manual development and ensured that the flow of data was efficient and uninterrupted.

Finally came the most visual and user-oriented aspect of the new data architecture: Tableau. Having already used Tableau, Optio was familiar with its powerful analytical capabilities, particularly via sharp data visualisations. Augmenting this experience even further was the addition of Curator by InterWorks, which allowed Optio to take the dashboards they already created in Tableau and put them into a completely customisable and branded web interface. Being able to tailor every aspect of the user experience would be a crucial boost in ensuring that end users can discover relevant insights quickly and organically.

“As the person responsible for getting the spend approved and delivering this project, I found the people at InterWorks (namely Chris Hastie and Richard Smith) amazing to work with. I work with a lot of vendor partners across our entire business, and I always looked forward to our discussions as both Chris and Richard ‘got’ the vision immediately and never swayed from that vision. They – along with Meishan, Carlos and Andy – simply made it all happen.”

– Kevin Cleary, CIO, Optio

Diving Deeper into the Data

With their new data architecture off the ground, Optio has cemented the data warehouse as the central component in their digital ecosystem. Further, they’ve already created a strategic roadmap for what should happen next. A big objective is to automate as much as possible. To that end, they’re already embarking on a new project with Lloyd’s Lab graduate DistriBind to automate production of Risk and Premium Bordereaux directly from their data architecture. The boost in efficiency, accuracy and time savings is not only operationally sound in the present, but it will also help to future-proof their business. This, and many other proposed data initiatives, all center around their data architecture that’s built to empower people, underscoring Optio’s mission statement of connecting people and technology.

The post Optio appeared first on InterWorks.

Have you ever wanted to make it big as a musician? Then you might know just how hard it is to share your music with the world, let alone create it. Once upon a time, you had to sign to a label to get anywhere close, but that changed in 2006 when Brooklyn-based TuneCore provided an audacious alternative path to success: The ability for artists to distribute their music online to leading digital music stores like Spotify and Apple Music while keeping 100% of the profits. The idea caught on, and TuneCore is now the world’s leading digital music aggregator. Distribution is just the start. TuneCore also provides numerous artist services, including music publishing administration and artist education.

A New Groove with Snowflake, Matillion and Tableau

TuneCore is no stranger to data. With countless plays on countless platforms from countless people across the world, the sheer volume of data generated by TuneCore artists is staggering. The advent of music streaming has contributed mightily to this exponential explosion of data. Their existing MySQL reporting environment and its accompanying data pipeline kept up for a time, but as more and more data made its way through the pipeline, bottlenecks began appearing. Query load times began taking longer, as did nightly data refreshes. Trying to troubleshoot and optimize these issues took significant time and effort from the TuneCore Data Analytics Team. Challenges with data polymorphism and tables not running efficiently began emerging more frequently, which made it difficult to turn reports around in a timely manner for other departments.

Knowing that their existing data architecture and reporting framework wouldn’t last for much longer, TuneCore Director of Data Analytics, Raj Sarma, began researching solutions for a new data architecture that could better meet their growing needs. Raj eventually fell in favor of a stack that included Snowflake as a scalable cloud data platform and Tableau for insightful reporting, but he was also looking for an ETL solution that would help them transform data and orchestrate workflows with ease. Matillion stood out because of its ability to handle data prevalent at TuneCore, as well as its easy-to-use GUI for setting up workflows.

With the technology stack selected, Raj began conversations with those vendors. During these conversations, Tableau recommended InterWorks as the ideal partner to help TuneCore implement their new stack. After a few conversations, Raj felt that a partnership with InterWorks would be a great fit and decided to engage InterWorks for a proof of concept – think of it like the data equivalent of a demo tape. This proof of concept would demonstrate whether the stack of Tableau, Snowflake and Matillion could indeed handle TuneCore’s needs before they made an all-in investment.

“When you’re on an airplane and they go through emergency protocols, they tell you to put on your oxygen mask first and then you’ll be able to better help others. It’s the same with data. We knew that we wouldn’t be able to empower our customers with better insights if we didn’t first enable ourselves, which is why we started with an internal proof of concept.”

– Raj Sarma, Director of Data Analytics, TuneCore

Jamming out with an InterWorks Proof of Concept

After some initial planning, TuneCore and InterWorks decided that the proposed proof of concept would be split 50/50 between data architecture and Tableau dashboarding work. On the InterWorks side, Data Lead Michael Treadwell, Data Architect Justin Lemmon and Analytics Consultant Matthew Albacete got to work on the data architecture portion and began gathering requirements alongside the TuneCore Data Analytics Team to tailor Snowflake and Matillion to their specific needs.

As the InterWorks team began standing up Snowflake as a cloud data platform, we found that its ability to perform “zero copy cloning” proved immensely valuable for TuneCore’s Dev > Prod > Deployment environments. It allowed them to make rapid changes to their different environments without having to copy everything over and duplicate storage with traditional data warehousing paradigms.

For the data transformation side of things with Matillion, InterWorks utilized Matillion’s CDC feature – a process that also allows for incremental data refreshes using source system logs – which reduces query load on the source system.

The InterWorks team also helped with TuneCore’s polymorphism data modeling challenges and created a workflow that would get TuneCore’s data in a manageable format for their analytics layer. While this might seem like a small component of the overall project, it was one of the key differentiators that made TuneCore’s challenge so unique, and InterWorks’ ability to work through it with them was a huge benefit to making the overall stack work well.

Once the data architecture phase was complete, nightly refresh times had been reduced from nine hours to 30 minutes.

“Every company should understand its limitations. We could have grown our team to achieve this in house, but that would have cost more and taken more time. In the interest of getting things done the right way and in a timely manner, engaging a consulting company like InterWorks was the right choice because they’re laser-focused on delivering that project.”

– Raj Sarma, Director of Data Analytics, TuneCore

Once all the foundational data architecture work was done, InterWorks Solutions Lead Karl Riddett came in to assist with Tableau dashboard planning. Karl immediately brought in InterWorks Analytics Consultant Danny Steinmetz to build the dashboards because of Danny’s unique experience as a consultant and as a musician. The process for Danny was easy and straightforward thanks to data architecture work done beforehand. Without having to worry about data prep, he could focus more on building dashboards and determining designs alongside Raj and his team. After gathering requirements with various stakeholders, Danny was able to build two baseline dashboards in a matter of days.

One dashboard focused on content review of music recordings, allowing TuneCore staff to easily check for copyright violations and other matters with far greater speed and efficiency. This dashboard allows them to see genres, languages and countries in a much more streamlined view. The second dashboard was created for the TuneCore Marketing Team to give them a better view of new client inflow and how that compares across different regions. Compared to their old QlikView dashboards, these Tableau dashboards gave them deeper insights while also being more user friendly.

“The biggest value from InterWorks was their ability to come in and hit the ground running with their business and technical expertise. They were able to understand our business very quickly and uncover a lot of hidden gems within our data. We feel really confident in what they’ve delivered to us.”

– Raj Sarma, Director of Data Analytics, TuneCore

The Sweet Sound of Modern Data Analytics

After working through the proof of concept over the course of five months, it was clear that the combination of Snowflake, Matillion and Tableau would be an excellent fit for TuneCore’s needs. More than that, it proved that this was a data and analytics stack that could scale with them in perfect lockstep as they continue to grow and bring in more data. For an independent company like TuneCore to compete at a high level in the music industry, it’s important to find solutions that are powerful yet cost intelligent. That’s exactly why the proof of concept from InterWorks was so valuable. It allowed TuneCore to discover whether this stack was right for their data and the people working with that data, without the pressure of a massive upfront investment.

The proof of concept didn’t just make believers of Raj and the TuneCore Data Analytics Team – it also impressed TuneCore leadership, as well as the other groups that interacted with the two dashboards built by the InterWorks Analytics Team. With those litmus tests passed, TuneCore pulled the trigger and went all in on the Snowflake, Matillion and Tableau stack in early 2021. They’ve officially rolled out the new data architecture and continue to iterate on several new dashboards.

“Knowing that we can now crunch the data and perform useful analyses, the Data Analytics Team is really invigorated. We’re at the point now where we’re telling teams across TuneCore that we’re opening shop and ready to work with them at a deeper level.”

– Raj Sarma, Director of Data Analytics, TuneCore

Beyond the technology itself, TuneCore also learned a lot about what kind of people they should add to their Data Analytics Team as they continue to grow. Working with InterWorks consultants gave them more clarity as to what roles and skills they really needed within their team, and they have since added several new team members with plans to add more in the near future.

So, what’s the bottom line on this new data architecture and analytics environment for TuneCore? Within the organization itself, TuneCore is now able to access insights faster, more easily and with more confidence than ever before. By eliminating manual processes and implementing scalable solutions, they’re able to think more proactively on new sources of insight vs. spending all their time on query troubleshooting or data pipeline maintenance. This means that the Data Analytics Team is able to help more groups at TuneCore in more meaningful ways. With clear and ample insights at their fingertips, those groups are more empowered to help TuneCore grow and better serve their artists. In an industry where every stream on every platform matters, that kind of insight is what amplifies success.

“Data is what drives artists’ understanding of their performance on digital platforms, and they will do better if they have tools that are engaging and intuitive. We want artists to be artists, not data analysts, so we’re constantly striving to make their data easier to interact with.”

– Raj Sarma, Director of Data Analytics, TuneCore

The post TuneCore appeared first on InterWorks.

Headquartered in Melbourne, Victoria, the Australian Football League (AFL) is the country’s only fully professional competition of Australian Rules Football (affectionately known as “footy”). With over 100 years of history, they have a rich tradition of guiding the sport from infancy to being arguably the most popular sport in Australia. With a league of 18 clubs across five of Australia’s six states, it’s a sport filled with strong regional and national pride.

Background

Like many sport leagues across the world, the AFL strives to stay on the cutting edge of technology, which includes all manner of organisational, club and fan insights. Over the past five years specifically, the AFL has pushed hard for the digitisation and modernisation of their analytics efforts. Coming from a heavily distributed model that relied on individual clubs to locally manage these efforts, there was a lot to do.

The first step the AFL took was acquiring a visual analytics platform that could bring together disparate data sources and visualise insights beautifully and easily. For that, they selected Tableau Software. InterWorks was proud to assist in these early dashboarding efforts to grow a data-focused culture.

A Better View of Customer Analytics

After acquiring Tableau, the AFL reached out to InterWorks again for data engineering and data architecture assistance as they looked to take some of their big data initiatives into the cloud. After some detailed discussions and scoping of what the AFL wanted to achieve, the Customer 360 project emerged.

The purpose of Customer 360 was to develop a 360-degree view of customer insights utilising a modern cloud architecture. Using Snowflake’s cloud data platform, they brought together 26+ disparate data sources on things like ticket purchases, merchandising sales, concessions, and attendance (as just a few examples) into one “golden” customer record that the AFL and member clubs could use to better reach fans. Customer 360 would also lay the groundwork for even more analytics collaborations in the future designed to help clubs more at the individual level.

Changing Trajectory with COVID-19

As was the case for many businesses, everything changed at the AFL in March 2020 when the global COVID-19 pandemic hit. For an organisation dependent on live sporting events with large gatherings of fans, COVID-19 was especially disastrous. Consequently, the Customer 360 project was put on hold so that the AFL could focus on navigating these uncharted waters. Fast-forward a few months, and the AFL contacted InterWorks again – this time with an idea that could help them get the AFL season safely restarted in conjunction with each of the Australian state governments.

The goal was this: If the AFL were to resume their season and host live sporting events with crowds of any size, they would need to develop an effective way to provide contact tracing data to state authorities and public health entities. If these authorities informed the AFL that an attendee tested positive for COVID-19, the AFL would need to provide back a list of all attendees, employees, patrons, etc. with whom the infected individual might have come into close contact.

To achieve this, the AFL put all their data in one place – Snowflake’s cloud data platform. Going into Snowflake were dozens of tables from all kinds of data sources from individual clubs, stadiums and third-party vendors. These data sources extended beyond the expected, covering not only ticket sales and entry scans but also metrics surrounding parking lot operations and restaurant reservations. Next came the careful planning and coordination with the AFL’s many vendors to ensure they understood what needed to be provided as outlined by each state government. Once all the data was collected, InterWorks used Matillion to ensure the data, no matter how disparate (JSON, email attachments, API, etc.), could be put into an easily consumable, single-source format.

With data from across the AFL primed and ready for analysis, the final piece of the puzzle was building an integrated view of the stadium experience. Thanks to the initial leg work from the Customer 360 dashboards, the AFL and InterWorks were able to quickly adapt their work with contact tracing considerations in mind. Not only is this view useful for the AFL and its member clubs, but it also helps them meet the broader objectives set by state authorities and public health entities.

Specifically, whenever state authorities report a positive COVID case, the AFL is able to send them a data export detailing if that person was at a given AFL match, as well as who else might have been exposed. From there, those same state authorities can send notifications to those potentially exposed, thus giving them advance warning and preventing further spread. All of this can be achieved in under an hour.

“Having a contact tracing solution in place has been critical in getting AFL fans safely back into stadiums. From day one, our goals for the build of the contact tracing solution have been that it needed to be fast, automated and secure. Partnering with InterWorks and leveraging technologies like Snowflake and Matillion allowed us to achieve these goals and stand up the solution in just a few months.”

– Elisa Koch, Head of Data & Analytics, AFL House

Re-Opening with Peace of Mind

When the AFL presented their contact tracing solution to state authorities, it provided confidence that AFL was doing everything it could to protect its fans and the broader community. Nobody was doing contact tracing at this level in Australia, and this created further confidence to give the AFL the green light to restart live sporting events.

For the AFL, this gave them the ability to bring fans back to games safely and ticketing revenue back into the league. More importantly, it would re-connect passionate fans with the footy they love, bringing back much-needed normalcy after almost a year apart.

The post The Australian Football League appeared first on InterWorks.

As a leading natural cosmetics manufacturer based in the Black Forest of Germany, Börlind has grown continuously since the 1970s to become a global brand. Börlind’s products can be found on the most exclusive shopping streets in the world. With products ranging from skincare over make-up to hair products, Börlind and their two brands, “Annemarie Börlind Natural Beauty” and “DADO SENS Dermacosmetics,” use solely natural raw materials for their award-winning products.

So Many Data Sources!

As one of the most established firms in natural cosmetics, Börlind’s products can not only be found in retail stores across the world but also on their beautifully designed website. Being active in the e-commerce business requires timely, data-driven decisions. This necessitates up-to-date, valid data from various sources, which must be presented in a way that can be easily compared and contextualized.

Decision-makers at Börlind are thus faced with a myriad of numbers and KPIs from sources like Google Ads, Google Analytics, Facebook, Instagram, YouTube, Twitter, e-commerce platforms, email marketing platforms, and on-premises ERP and CRM systems. Google, Facebook and e-commerce platforms already offer dashboards and real-time insights within their own services, but this only gives the marketer a fraction of the whole picture. Calculating metrics like the return on marketing investments (ROI) or conversion rates across platforms can be very cumbersome for a company like Börlind using traditional means. Additionally, they need to access these insights quickly in order to make timely decisions in a rapidly moving market. Meanwhile, programming flows to retrieve data from these services is cost and time-intensive.

Tableau to the Rescue

As a data-driven organisation, Börlind has already implemented Tableau and thus has the means to analyse data from various sources using Tableau’s built-in connectors. While these connectors are great for retrieving data from each source individually, in order to make sense of the data and discover overall patterns, data from various sources had to be brought together in one place with a coherent structure. These intermediary steps of data pre-processing and data integration were needed due to several constraints that come with connecting to these data sources right from Tableau:

• Google has strict rules on how many rows and columns can be pulled from its API in a single query – answering complex questions with that can be difficult.
• While their e-commerce platform, Magento, is based on a MySQL database, this database is not built for the heavy load that comes from Tableau queries.
• Some data living in web data sources can only be retrieved using complex API queries, which meant that some kind of connector (e.g. a Tableau web data connector) would need to be programmed. This implied the need for upskilling someone to write the code necessary and maintain any future efforts.

The Need for a Scalable and Manageable Solution

Matillion was the solution of choice for staging, pre-processing, and loading data into Snowflake, the chosen cloud data warehousing solution. The reasons for this choice are manifold: Both Matillion and Snowflake are very easy and quick to set up. A Matillion instance is created by signing up for an Amazon Web Services (AWS) account and starting the EC2 instance. The initial setup for Snowflake encompasses signing up for an account and logging in – that’s it! With this quick and easy setup, there was nothing in the way of starting to use the tools, which was supported both by readily available online learning resources as well as excellent customer support from Snowflake and Matillion. Hence, new users will be able to tap into this new area of analytics easily as there is plenty of support.

Furthermore, in the case at hand, it made sense to first learn the new tools and then identify the users’ needs along the way. An added benefit is that once this solution is scaled, it shouldn’t incur a huge upfront investment for hardware and software. Therefore, the advantages of the cloud could fully be harnessed. In respect to Börlind’s bottom line, the pay-as-you-go models of both Matillion and Snowflake didn’t tie up capital and resources unnecessarily. A big advantage of Snowflake is that, while it is highly scalable, secure and incredibly fast, you can connect to it with Tableau’s native Snowflake connector like any other relational database, making use of all of Tableau’s functions and possibilities. My colleague, Holt Calder, explains why Tableau and Snowflake are such a great match in this informative blog post.

Lastly, we could leverage the information that Snowflake makes readily available about how it is used and create transparency around how much Börlind’s online analytics cost. These costs could be monitored on a high level, answering questions like, “how much are we paying for extracting and loading data?” down to the query level of “how much does it cost us to analyse ad performance on Google?” This level of cost transparency is hard, if not impossible, to achieve with an on-premises database solution. For those who are curious, Tableau released this ready-made dashboard to monitor your Snowflake usage and costs.

The Path to Continued Analytic Success

Having a deep and long-term relationship with Tableau, Snowflake and Matillion, InterWorks was well-positioned to help Börlind build a successful online analytics platform. In an initial proof of concept, data was moved from all specified sources into Snowflake to test the feasibility of Matillion as an extract and load tool. Leveraging Matillion’s easy-to-use API profile system, integrations that are not natively supported by Matillion, such as Cleverreach, could easily be set up.

Once all connections to source data had been established, a target data model was defined and created in Snowflake. Lastly, any data transformations that needed to be done to the data in Snowflake were set up in Matillion. All jobs were scheduled using the job scheduler in Matillion and CloudWatch to start up and shut down the Matillion instance. Another colleague of mine, Benjamin Du, shares how easy it is to load data into Snowflake using Matillion in this blog.

The biggest advantage for Börlind now that their new data warehousing solution is in place is that the data now securely available in Snowflake can be easily accessed by Tableau users from Tableau Desktop and Tableau Server. With rapid insights readily available, they can steer their online analytics practice to answer business questions with great speed and confidence.

The post Börlind appeared first on InterWorks.

InnovAge is a senior care provider committed to helping individuals live life on their terms by enabling them to age in peace in their own homes and communities safely and comfortably. They achieve this through PACE – a Program of All-Inclusive Care for the Elderly. Supporting seniors in Colorado, California, New Mexico, Pennsylvania and Virginia, they are the largest provider of PACE in the United States.

Growing Company, Growing Data

With a mission to provide excellent care and community for as many seniors as possible, InnovAge charters themselves as a growth-forward company. Though their footprint is already considerable, they are actively acquiring new care centers each year. As they have grown, so has the amount of data they generate.

Growing data volume is only one byproduct of expansion. Given the scope of InnovAge’s network and variety among their care centers, data from each center often comes in different shapes and sizes. While steadily acquiring new centers, InnovAge noticed that integrating new data sources into something that could be analyzed on a macro level grew more challenging. Even minor inconsistencies within a single field could have a snowball effect.

Prioritizing their goal of unifying this data for more effective analysis, InnovAge knew they needed a better way to store and prepare their data. With this foundation in place and the data readily accessible, report builders could provide more insights for the business side of the organization. Of course, choosing the right data stack to make this possible was no easy task.

“At the end of the day, we’re trying to produce a robust data warehouse that spans across all of our participant touchpoints, both in our centers and at external providers, and get it in a way that’s understandable for end users to interact with using a tool like Tableau.”

– Greg Jensen, Director of Reporting and Analytics, InnovAge

Planning the Right Foundation with InterWorks

The first step in retooling any data environment is considering all the available options. Led by InnovAge Director of Reporting and Analytics, Greg Jensen, the InnovAge team evaluated multiple different stacks and fielded several proposals from a variety of data consultancies. After seeing options ranging from “lift and shift” with Microsoft Azure to Data Vault combined with cloud-based data warehousing in Snowflake, they felt the latter option was best for their needs.

The next step was then finding the right data consultancy to help them. They sought a partner with deep knowledge of Data Vault and Snowflake, a holistic understanding of the surrounding environment and, most importantly, a great degree of trust. Many partners came back with impressive proposals, but InterWorks caught Greg’s eye for a few reasons.

Having worked with InterWorks at a previous employer, Greg was familiar with the quality of InterWorks’ data consulting. Perhaps the biggest advantage was that InterWorks’ data expertise didn’t stop at Data Vault and Snowflake but went further into the data presentation layer. That ability to understand data pipelines, data warehousing, data integration and analytics was exactly what InnovAge was looking for in a partner. InterWorks having team members located within commuting distance of their Denver HQ was also a bonus.

Even before signing a contract, InterWorks Data Engineer Karl Young was working with Greg Jensen and the InnovAge team on confirming whether Data Vault and Snowflake were the right anchor points for their data stack. Though Data Vault and Snowflake aren’t common among healthcare providers yet, they determined this solution was indeed the best possible way forward, especially given InnovAge’s active growth strategy. With those two technologies determined, they could then build the appropriate surrounding infrastructure.

“We were impressed with the proposal that InterWorks put together. It seemed like they really understood our business and what we were trying to do.”

– Greg Jensen, Director of Reporting and Analytics, InnovAge

Building the InnovAge Data Stack

The InnovAge data stack all starts with the actual data generated from InnovAge participant centers. This data is captured in several different systems, but the two biggest are their electronic medical records system (EMR) and Census Tracker application. Like all EMRs, InnovAge’s tracks critical patient information and health records. They use the same EMR in most states, but each state must legally have its own instance. Conversely, the Census Tracker is a global application used by participant centers. This application records enrollment dates, as well as additional demographic information about InnovAge participants, and serves as the hub for InnovAge’s data analysis efforts. Because of that significance, the Census Tracker was a great place to start in terms of data source integration.

The data from their Census Tracker application lives on a critical, on-premises SQL Server database, so the first step involves using Fivetran to incrementally pull that data and stage it in the cloud-based Snowflake database (hosted, in this case, on Microsoft Azure). The reasoning behind this method was that it allows data to be accessed from InnovAge’s production database without impacting front-end applications that are using the same database. The impact is minimized by Fivetran’s use of the Change Tracking logfiles of SQL Server, providing a simple environment to do the job.

With the data changes staged in Snowflake, InnovAge was able to take advantage of some key benefits that aligned with their vision. A powerful SQL engine, affordable storage and overall ease of use certainly factored into that vision, but it was Snowflake’s scalability that was the real difference-maker. Given that Snowflake is designed to rapidly scale up or down at will, InnovAge no longer has to worry about whether or not they have enough resources to integrate new data sources as they emerge. That runway for future growth is invaluable considering their active growth strategy.

The next piece of their stack is their data transformation and scheduling layer. What once required manual ETL processes has since been streamlined and automated with Matillion. Karl built out a number of workflows that allow InnovAge to freely move and mesh their data into a Data Vault structure. Once in that structure, the data can then be brought into Tableau Desktop. That’s where the analysis magic happens, and insights are revealed in the form of engaging Tableau dashboards.

Immediate Data Discoveries

A lot has been achieved in implementing InnovAge’s new stack, but like all data projects, there’s always a new frontier to explore. Still, the amount of work InnovAge and Karl have achieved to date is impressive. Most notably, they’ve discovered quite a few insights around their data quality and the efficiency of their data environment as a whole. Thanks to Data Vault, they’re able to identify data inconsistencies faster and with greater scope. That means faster resolution of those inconsistencies as they appear. Meanwhile, Snowflake and Matillion have vastly improved data source integration and transformation.

Though they are still integrating new data sources into their environment, this new foundation provides a level of clarity and efficiency that will set InnovAge up for long-term success. Having all their data in one place is no doubt the biggest benefit, but increased speed to data is also massively impactful to InnovAge’s Tableau report builders. With the right foundation in place, Greg and his team can now delve into new opportunities involving advanced statistical analysis and data science. That all goes back to investing in the right solutions, partner and plan early on. Thanks to that foresight, along with some smart work from them and the InterWorks team, InnovAge’s analytics practice has taken a quantum leap forward. That translates to better insights for the business and, more importantly, better care for InnovAge participants.

“I don’t feel like we would be where are today if it wasn’t for InterWorks and, specifically, Karl. We could have a myriad of other consultants doing this, but it’s hard for me to envision having the collaborative personal interactions we’ve had with anyone else. The reality is that Karl is part of our team. I have zero regrets about the path we’ve taken.”

– Greg Jensen, Director of Reporting and Analytics, InnovAge

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